Data Quality Chill Factor
The recent North American cold wave that winded its way across Canada and the United States, brought heavy snowfall and broke low temperature records, leading to business, school, and road closures, as well as flight cancellations.
The recent North American cold wave that winded its way across Canada and the United States, brought heavy snowfall and broke low temperature records, leading to business, school, and road closures, as well as flight cancellations. This polar vortex also spun the phrase wind chill factor into almost every conversation, prompting me to investigate how wind chill is calculated, and leading me to yet another cold contemplation of data quality.
Before we get to data quality, let’s begin with some chilling facts about wind chill factor.
Wind makes us feel cold because as it blows across the exposed surface of our skin, it draws heat away from our bodies. When the wind picks up speed, it draws more heat away from exposed skin, cooling us more quickly. Wind chill, therefore, calculates how rapidly body heat is lost at different wind speeds.
Though not originally meant to express a temperature equivalent, weather forecasters started translating wind chills into the “feels like” factor we hear in weather reports today. For example, I live in Iowa and at one point last week the air temperature was -3 degrees Fahrenheit, while the wind chill factor made it feel like -36 degrees Fahrenheit.
It’s also important to note that lower wind chills mean inanimate objects cool to the air temperature more quickly, but even high winds can’t force the object’s temperature below the air temperature. For example, if the air temperature is 40 degrees Fahrenheit, water will not freeze even if the wind chill makes it feel to us like it’s below 32 degrees Fahrenheit (i.e., the freezing point of water).
What does Data Quality feel like?
All of this made me wonder if data quality has a chill factor. Data quality metrics are analogous to air temperature, meaning they’re often an objective measurement of the quality of data. A postal address, for example, can be validated independent of business context—it’s either valid or invalid.
However, what an invalid postal address feels like is dependent on a subjective measurement of business context. An email marketing program, for example, would not care about the validity of postal addresses since its data usage has no exposed skin in the postal address game, so to speak. Whereas a non-electronic billing system would feel the data quality chill factor of an invalid postal address.
If you want your organization’s data quality to be warm and cozy for all of your users, make sure you consider what data quality feels like from their business perspective, perhaps supplementing objective data quality metrics with a subjective data quality chill factor that’s customized for each user.
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